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LLM Chat Completions via APEX Faucet (x402)
x402
LLM Chat Completions via APEX Faucet (x402) is a paid API for AI agents from apexfaucet.xyz, paid per call via x402, $0.002/call, status unknown (last checked 2026-11-02).
OpenAI-compatible chat completion API supporting multiple open-source LLMs, billed at $0.002 USDC per call via x402 micropayment protocol.
Price / call$0.002
Success—
StatusUnknown
How this service works
Free APEX on X1, and free USDC plus APEX on Arc, three times a day. No gas, no sign-up. Plus a swap, a screener, APEX City and token checks.
Output
Returns a JSON object with `ok: true`, a `data` field containing the OpenAI-compatible chat completion response (including choices, message content, and tool calls if applicable), and a `paid` field confirming the $0.002 USDC micropayment amount deducted.
Example use cases
On-demand LLM reasoning for agents
“Ask llama-3.3-70b to analyze this text and extract all named entities: 'Elon Musk announced that Tesla will open a new Gigafactory in Austin, Texas by Q3 2025.' Use medium reasoning effort and keep it under 300 tokens.”
Tool-calling workflow automation
“Use gpt-oss-120b with my get_weather function tool defined as {name: get_weather, parameters: {location: string}} and send the user message: 'What's the weather like in Paris right now?' so it can decide to call the tool.”
Quick summarization with small model
“Use llama-3.1-8b to summarize this article in 3 bullet points: [article text]. Keep the output under 200 tokens to save cost.”
Technical details
Endpoint
POSThttps://apexfaucet.xyz/api/x402/chat-completions?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a pay-per-call LLM API with no subscription or API key, especially in agentic or automated workflows using the x402 micropayment protocol. It's ideal for low-volume or bursty inference needs where you want to pay $0.002 per call in USDC rather than managing a monthly plan. Good for agents that need OpenAI-compatible chat completions with tool/function calling support using open-source models like Llama or Qwen.
Side effects
- Insufficient USDC balance causes x402 payment failure and request rejection
- max_tokens exceeds 1024 causes validation error
- Empty or malformed messages array returns 400 error
- Invalid model name not in enum causes rejection
- Streaming not supported — requests with stream:true will fail or be ignored
- Context window overflow if messages array is too large or content too long
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| model | string | Default gpt-oss-120b. |
| tools | array | OpenAI-style function tools (optional). |
| messages | array | |
| max_tokens | integer | |
| temperature | number | |
| reasoning_effort | string | gpt-oss and qwen3 only; default low. |
Output
Schema · Response
{
"type": "json",
"schema": {
"type": "object",
"properties": {
"ok": {
"type": "boolean"
},
"data": {
"type": "object"
},
"paid": {
"type": "number"
}
}
},
"example": {
"ok": true,
"data": {},
"paid": 0.002
}
}Tags
x402
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